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Data science and artificial intelligence: Finance, policy and governance

Erlin Guillermo Cabanillas Oliva, Ulises Octavio Irigoin Cabrera, Juan Carlos Lázaro Guillermo,

Cesar Augusto Agurto Cherre, Oscar Raúl Esquivel Ynjante, Carlos Mariano Alvez Valles

Guillermo, Cesar Augusto Agurto Cherre, Oscar Raúl Esquivel Ynjante, Carlos Mariano Alvez

Valles, 2024

Second edition: September, 2024

Edited by:

Editorial Mar Caribe

www.editorialmarcaribe.es

Av. General Flores 547, Colonia, Colonia-Uruguay.

Cover design: Yelitza Sanchez Caceres

Translation of the original Spanish edition into English: Ysaelen Josefina Odor Rossel

E-book available at https://editorialmarcaribe.es/data-science-and-artificial-intelligence-finance-

policy-and-governance/

Format: electronic

ISBN: 978-9915-9706-5-3

ARK: ark:/10951/isbn.9789915970653

Non-commercial attribution rights notice: Authors may authorize the general public to reuse their works

for non-profit purposes only, readers may use a work to generate another work as long as research credit

is given and they grant the publisher the right to first publish their essay under the terms of the license

CC BY-NC 4.0.

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Editorial Mar Caribe

Data science and artificial intelligence: Finance, policy

and governance

Colonia, Uruguay

2024

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About the authors and the publication

Erlin Guillermo Cabanillas Oliva

egco1egco1@gmail.com

https://orcid.org/0000-0001-9815-6828

Universidad Nacional de la Amazonía

Peruana, Peru

Ulises Octavio Irigoin Cabrera

uirigoin@ucp.edu.pe

https://orcid.org/0009-0007-6168-7415

Universidad Científica del Perú, Peru

Juan Carlos Lázaro Guillermo

jlazarog@unia.edu.pe

https://orcid.org/0000-0002-4785-9344

Universidad Nacional Intercultural de la

Amazonía, Peru

Cesar Augusto Agurto Cherre

cesar_agurto@unu.edu.pe

https://orcid.org/0000-0001-6494-3567

Universidad Nacional de Ucayali, Peru

Oscar Raúl Esquivel Ynjante

oesquiveli@unia.edu.pe

https://orcid.org/0000-0002-5097-831X

Universidad Nacional Intercultural de la

Amazonía, Peru

Carlos Mariano Alvez Valles

calvezv@unmsm.edu.pe

https://orcid.org/0000-0003-2341-6191

Universidad Nacional Mayor de San

Marcos, Peru

Research result book:

Original and unpublished publication, the content of which is the result of a research process

carried out before its publication, has been double-blind peer-reviewed by external peers, the

book has been selected for its scientific quality and because it contributes significantly to the area

of knowledge and illustrates fully developed and completed research. In addition, the publication

has undergone an editorial process that guarantees its bibliographic standardization and

usability.

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Index 
Introduction ......................................................................................................................... 6 
Chapter 1 .............................................................................................................................. 9 
Artificial Neural Networks: Financial risks in credit institutions ................................ 9 
Benefits of AI in Finance ................................................................................................... 10 
Portfolio and asset management ..................................................................................... 10 
AI-powered hedge funds and ETFs ................................................................................ 13 
Algorithm trading ............................................................................................................. 14 
The unintended consequences and potential risks of AI ................................... 18 
Chapter 2 .............................................................................................................................. 23 
BigTech, financial services and blockchain ......................................................................... 23 
AI and Blockchain-based financial products................................................................. 25 
AI increases the capabilities of smart contracts .................................................. 28 
Self-learning smart contracts and DLT governance ........................................... 31 
Emerging risks from AI/ML/Big Data use: risk mitigation tools ............................... 35 
Data and its management ....................................................................................... 35 
Chapter 3 .............................................................................................................................. 41 
Data and competition in AI-based financial services ........................................................... 41 
Bias and discrimination .......................................................................................... 43 
Explainability ........................................................................................................... 45 
Robustness and resilience of AI models ............................................................... 52 
Chapter 4 .............................................................................................................................. 59 
Governance of AI systems ................................................................................................... 59 
Regulatory Considerations ..................................................................................... 64 
Occupational hazards ............................................................................................. 67 
Political implications ......................................................................................................... 69 
Political activity around RNA in finance ............................................................. 69 
Political considerations ........................................................................................... 77 
Conclusions .......................................................................................................................... 85 
Literature .............................................................................................................................. 87 
 
   

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Introduction

Incorporating artificial intelligence (AI) and big data into sentiment analysis to

detect patterns, trends and trading signals is a growing trend that has been around for

some time. For years, traders have carefully analyzed news and management reports,

trying to understand how non-financial information affects stock prices (Assad et al.,

2020).

However, the use of advanced technologies such as text mining, social network

analysis, and natural language processing (NLP) algorithms has taken this method to a

new level. These innovative tools allow marketers to make informed decisions by

automating data collection and analysis, as well as identifying consistent patterns or

behaviors on a scale that humans cannot handle (Schrepel, 2020).

Therefore, AI-powered trading differs from systematic trading in its use of

reinforcement learning and its ability to fine-tune the AI model to changing market

conditions. In contrast, traditional methodological strategies often take longer to fine-

tune parameters due to extensive human involvement. Traditional back testing strategies

based on historical data may not produce optimal real-time performance when pre-

established trends are no longer valid. On the other hand, the implementation of machine

learning models allows analytics to focus on predicting and analyzing trends in real-time.

These tests predict and adapt to trends in real-time, thereby minimizing the risk of

overfitting or curve-fitting seen in back testing based solely on historical data and trends.

The application of artificial intelligence in trading has gone through many stages of

development and has become increasingly complex, integrating at each stage with

traditional algorithmic trading. Initially, algorithms were simple, with predefined buy or

sell orders and basic parameters.

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Later, more advanced algorithms were introduced that allowed for flexible pricing.

The next generation of algorithms focuses on minimizing market impact by splitting large

orders, called “execution algorithms,” to achieve optimal pricing. Today, advanced

strategies use deep neural networks to optimize order placement and execution, with the

goal of minimizing market impact (Tan, 1997). Inspired by the human brain, deep neural

networks use algorithms that can recognize patterns and require less human intervention

to operate and learn. Using these techniques, market makers can improve inventory

management and reduce balance sheet costs.

As artificial intelligence continues to develop, algorithms are moving towards

automation, relying more on computer programming and learning from input data,

thereby reducing the need for human intervention. In practical applications, more

advanced forms of AI are currently used primarily to detect signs of trouble in flow-based

trading that may not have much news value. These incidents are less visible, pose greater

challenges to identify, and extracting value from them is a more difficult task.

Rather than simply improving execution speed, AI is actually used to filter out

data noise and turn that information into actionable decisions. On the other hand, less

complex algorithms are mostly used for information-rich events, such as financial news,

which are easy for all participants to understand and require rapid implementation.

Therefore, at the current stage of development, ML-based models serve a different

purpose than HFT strategies, which focus on acting quickly and gaining an edge in

trading. Instead, ML models are mostly used offline for tasks such as fine-tuning

algorithm parameters and optimizing decision logic rather than performing actual trades.

However, as AI technology advances and its applications increase, it has the

potential to improve traditional algorithmic trading in the future (OECD, 2023).

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This is possible when AI technologies are integrated into the trade execution phase,

providing advanced automated trade execution features and covering every stage from

signal collection to strategy and trade execution.

ML-based execution algorithms will enable automatic and dynamic adjustment of

decision logic during trading. In such cases, current requirements for algorithmic trading,

such as safeguards in pre-trade risk management systems and automated control

mechanisms to stop algorithms when they exceed risk limits, should be expanded to

include AI-guided algorithmic trading.

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Chapter 1

Artificial Neural Networks: Financial risks in credit institutions

Since the groundbreaking research conducted by Beaver in the late 1960s, there

has been a great deal of interest in using financial ratios as a means of predicting financial

failure. This surge in interest can be attributed to the influential work of Altman (1968),

where he combined five financial ratios into a single predictor known as the Z factor,

specifically designed to assess the likelihood of business failure (Tan, 1997). A notable

advantage of Altman's methodology is its ability to establish a standard benchmark for

comparing companies within the same industry, while providing a consolidated measure

of financial strength derived from a company's financial accounts. However, despite its

appeal, this methodology is not without limitations, as ratios can vary significantly across

different industry sectors and accounting methods used.

Limitations become more apparent when using financial indicators to forecast the

financial challenges faced by financial institutions. The inherent high leverage of these

institutions makes it difficult to apply models that were originally developed for the

corporate sector. However, there has been increasing acceptance of using these models in

the financial sector by considering financial institutions as a distinct category of firms. In

Australia, there have been instances where researchers have conducted unprecedented

analyses of financial distress among non-bank financial institutions. These studies

employ a Probit model to address the limited nature of the dependent variables observed

in the financial distress data.

In this section of the book, the main focus is on the effectiveness of ANNs as an

early indicator of financial distress within credit unions. To provide an unbiased

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assessment, the ANN-based model developed in this study is compared to the Probit

model created by Hall and Byron, using the same data set. The findings suggest that the

ANN method slightly outperforms the Probit model when examining the same data set.

In addition, modifications to the ANN model design are explored to improve its

performance as an early warning predictor.

Benefits of AI in Finance

The adoption of artificial intelligence (AI) in the financial industry is being driven

by the significant and ever-increasing availability of data, as well as the advantage that

AI and machine learning (ML) can bring to financial services firms (OECD, 2021). With

the explosion of data and advances in computing power, particularly through cloud

computing, machine learning models can effectively analyze this vast amount of data and

uncover hidden patterns and relationships that are beyond human capabilities.

As a result, financial sector companies are increasingly using AI/ML and big data

to gain a competitive advantage. This includes improving operational efficiency by

reducing costs and improving the quality of financial services products to meet customer

demands. This trend is expected to further amplify the competitive advantage of financial

companies in the future (OECD, 2021).

Portfolio and asset management

ML models have the ability to continuously monitor and analyze thousands of risk

factors on a daily basis. Additionally, they can simulate and evaluate portfolio

performance under thousands of economic and market scenarios. This level of advanced

analysis and risk assessment can improve risk management practices for asset managers

and other large institutional investors. One specific application of AI, known as natural

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language generation (NLG), can prove especially valuable to financial advisors. NLG

enables advisors to analyze and present complex data in a more understandable and

relatable way for their clients. By “humanizing” and simplifying data analysis and

reporting, NLG can help advisors effectively communicate investment strategies and

insights to their clients.

Thus, the use of AI and ML in asset management offers a multitude of benefits.

From improving operational efficiency to enhancing risk management practices and

delivering a superior customer experience, these technologies have the potential to

revolutionize the industry. As the field of AI continues to advance, asset managers and

financial institutions are expected to increasingly adopt these technologies to stay ahead

in an ever-evolving market. In terms of operational benefits, the implementation of AI

technologies can result in significant cost reductions for investment managers.

By automating tasks that were previously performed manually, such as

reconciliation processes, AI can streamline operations and reduce administrative

expenses. Additionally, the increased efficiency and speed offered by AI can potentially

lead to greater cost savings for asset managers. The integration of artificial intelligence

(AI) and machine learning (ML) into asset management has the potential to improve the

efficiency and accuracy of various operational workflows. This technological

advancement not only promises to improve overall performance but also strengthen risk

management practices and enhance the overall customer experience.

By using large amounts of data, machine learning models can offer asset managers

valuable recommendations that can impact their decision-making process regarding

portfolio allocation and stock selection. With the advent of big data, traditional data sets

have become widely accessible to all investors, prompting asset managers to leverage this

resource to gain valuable insights into their investment strategies.

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Across the investment community, information has always played a crucial role,

with data serving as the foundation for various investment approaches such as

fundamental analysis and systematic trading. While structured data has long been the

focal point of these “traditional” strategies, the abundance of raw or unstructured/semi-

structured data now presents an opportunity for investors to use AI to gain a new

informational advantage. By employing AI, asset managers can efficiently process large

amounts of data from multiple sources and quickly extract valuable insights to inform

their strategies.

The use of artificial intelligence and machine learning, along with big data

analytics, tends to be more common among large asset managers and institutional

investors due to their financial capacity and available resources to invest in AI

technologies. As a result, smaller players may face difficulties in adopting these

techniques, as they lack the necessary investment in technology and skilled professionals

to handle large amounts of unstructured big data and develop machine learning models.

Even if the implementation of AI and proprietary models provides a competitive

advantage, it may further limit the participation of smaller players who cannot

incorporate in-house AI techniques or access big data sources (França et al., 2021). This

could therefore reinforce the current trend of concentration among a few large players in

the hedge fund sector, as these larger groups outperform their more agile competitors.

Limited participation by smaller entities in the sector will continue until the tools

they need are widely available or offered by third-party vendors. In addition, third-party

data sets may not meet the same industry standards, so users of these tools will need to

build trust in the accuracy and reliability of the information they rely on. This level of

confidence in the validity of big data is necessary for smaller players to feel comfortable

enough to adopt and use these tools.

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The use of identical AI models across multiple asset managers has the potential to

lead to herding behavior and create one-way markets. This could present certain dangers

to the overall liquidity and stability of the system, particularly during periods of

economic stress. The emergence of significant market volatility may be intensified by

simultaneous large-scale buying or selling activities, thereby introducing new

vulnerabilities into the system.

There is a possibility that incorporating AI/ML and big data into investment

strategies has the potential to reverse the prevailing trend of passive investing. If these

innovative technologies demonstrate a consistent ability to generate alpha, indicating a

cause-effect relationship between the use of AI and outperformance, it presents an

opportunity for the active investment community to reinvigorate its approach and

provide additional alpha opportunities to its clients.

AI-powered hedge funds and ETFs

Hedge funds have been leading the way in adopting and utilizing innovative

financial technology, such as big data analytics, artificial intelligence (AI) and machine

learning (ML), in their trading strategies and back-office operations. In more recent times,

a new generation of hedge funds, commonly referred to as “AI pure play” funds, has

emerged that rely exclusively on AI and ML technologies to drive their investment

decisions and portfolio management (e.g., Aidiyia Holdings, Cerebellum Capital,

Taaffeite Capital Management and Numerai).

So far, there has been a notable absence of any academic or impartial assessment

of the effectiveness of artificial intelligence (AI)-powered funds, conducted by an entity

outside the financial industry. Such an assessment would aim to compare the numerous

funds that claim reliance on AI technology (Westerhuis et al., 2008). As fund managers

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employ varying levels of AI integration in their operations and strategies, they naturally

hold back their methodologies to maintain a competitive edge. Consequently, it becomes

difficult to compare the performance of various self-proclaimed AI-powered products, as

the degree of AI utilization and the maturity of its implementation differ significantly

across these funds (Motta, 2023).

The private sector offers AI-powered hedge fund indices that clearly outperform

conventional hedge fund indices provided by the same source. It is important to note that

third-party indices are often influenced by biases such as survivorship bias and self-

selection of funds included in the index, as well as backfilling. It is therefore advisable to

approach these indices with caution.

Furthermore, there is growing evidence suggesting that machine learning (ML)

models outperform traditional forecasts when it comes to macroeconomic indicators such

as inflation and GDP. This improvement in performance is particularly evident in times

of economic stress when accurate forecasts are crucial. Thus, AI-based techniques have

proven superior in identifying previously unknown correlations in the occurrence of

financial crises. ML models have significantly outperformed logistic regression models

in predicting and forecasting financial crises in out-of-sample tests.

Algorithm trading

Artificial intelligence has the potential to revolutionize the trading industry by

offering trading strategy suggestions and powering automated trading systems. These

AI-based systems are capable of making predictions, determining the best course of

action, and executing trades without the need for human intervention. They use

advanced AI techniques such as evolutionary computing, deep learning, and

probabilistic logic to identify and execute trades in the market.

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Similarly, AI techniques such as algorithmic wheels can systematically strategize

upcoming trades by applying a logical “if/then” thought process. This level of AI

integration into trading enables predictive capabilities that far exceed those of traditional

algorithms in the financial and trading sectors, particularly considering the current

interconnectedness across asset classes and geographies.

Thus, AI-powered trading systems have the potential to assist traders in effectively

managing both their risk and order flow. These innovative applications can monitor and

analyze risk exposure, allowing them to automatically adjust or exit positions based on

user preferences and requirements. The remarkable aspect of these AI systems is that they

possess the ability to self-train and adapt to ever-changing market conditions, thereby

minimizing the need for human intervention. Likewise, these systems can facilitate the

seamless management of flows between brokers, ensuring smooth execution of

predetermined trades. And also, they have the ability to regulate fees and allocate

liquidity across various exchanges, considering factors such as regional market

preferences, monetary considerations, and other essential parameters involved in

managing an order.

In today’s technologically advanced markets, particularly in the fields of equity

and foreign exchange products, the implementation of AI solutions has great potential in

terms of providing competitive pricing, efficient liquidity management, and optimized

execution processes (Botta & Wiedemann, 2019). One of the crucial advantages of using

AI algorithms in trading is their ability to improve liquidity management and facilitate

the execution of large orders without causing substantial market disruptions. These

algorithms possess the ability to dynamically adjust order size, duration, and order size,

based on the prevailing market conditions, thereby ensuring optimal performance.

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The integration of artificial intelligence (AI) and big data into sentiment analysis

to detect patterns, trends, and trading signals is a growing trend that has been around for

quite some time now. Traders have been examining news and statements from company

management for years, trying to understand how non-financial information affects stock

prices. However, the use of advanced technologies such as text mining, social media

analysis, and natural language processing (NLP) algorithms has taken this practice to

new heights. These innovative tools allow traders to make informed decisions by

automating the process of data collection and analysis, as well as identifying consistent

patterns or behaviors on a scale that would be impossible for a human to handle.

Consequently, AI-driven trading is distinguished from systematic trading due to

its utilization of reinforcement learning and the ability to adjust the AI model according

to changing market conditions. In contrast, traditional systematic strategies often require

more time to fine-tune parameters due to extensive human involvement. Conventional

back testing strategies, which are based on historical data, might not deliver optimal

performance in real-time when previously identified trends no longer hold. On the other

hand, the implementation of machine learning models allows the analysis to focus on

predicting and analyzing trends in real-time. For example, predictive testing is employed

instead of back testing. These tests predict and adapt to trends in real-time, thereby

mitigating the risk of overfitting or curve-fitting observed in back testing based solely on

historical data and trends.

The application of AI in trading has gone through several phases of development

and increasing complexity, integrating with traditional algorithmic trading at each stage.

Initially, algorithms were simple, with predefined buy or sell orders and basic

parameters. Later, more advanced algorithms were introduced that allowed for dynamic

pricing (Brown & MacKay, 2021). The next generation of algorithms focused on

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minimizing the impact on the market by breaking up large orders, known as “execution

algorithms,” which aimed to obtain optimal prices.

Currently, innovative strategies use deep neural networks to optimize order

placement and execution, with the goal of minimizing market impact. Deep neural

networks, inspired by the human brain, employ algorithms that are capable of

recognizing patterns and require less human intervention to operate and learn. By using

these techniques, market makers can improve their inventory management and reduce

balance sheet costs. As AI continues to advance, algorithms are moving toward

automation, relying more on computer programming and learning from input data,

thereby reducing the need for human intervention (Metaxa et al., 2021).

In the realm of practical application, the most advanced forms of AI are currently

predominantly used to detect incident signals in flow-based trading that may not have

significant news value. These incidents are characterized by being less overt, posing

greater challenges in identification, and extracting value from them is a more arduous

task. Rather than merely improving execution speed, AI is actually employed to filter out

data noise and transform this information into actionable decisions. On the other hand,

less sophisticated algorithms are employed in information-laden events, such as financial

news, which are more easily understandable to all participants and require fast execution.

Therefore, at the current stage of their development, ML-based models serve a

different purpose compared to HFT strategies, which focus on quick action and gaining

an edge in trading. Instead, ML models are mostly used offline for tasks such as refining

algorithm parameters and improving decision-making logic rather than for actual trade

execution. While, as AI technology advances and finds more applications, it has the

potential to improve traditional algorithmic trading in the future. This could happen

when AI techniques are incorporated into the trade execution phase, providing advanced

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capabilities for automated trade execution and covering every step from signal capture

to trade strategy and execution.

ML-based execution algorithms would enable autonomous and dynamic

adjustment of decision logic during trading. In such cases, existing requirements for

algorithmic trading, such as safeguards in pre-trade risk management systems and

automated control mechanisms to stop algorithms when they exceed risk limits, would

need to be extended to include AI-powered algorithmic trading (OECD, 2023).

The unintended consequences and potential risks of AI

The widespread adoption of identical or similar models by numerous operators in

various markets may have unintended consequences for competition and could

exacerbate tensions within those markets (Descamps et al., 2021). If these models were to

become widely used by traders, they would naturally decrease arbitrage opportunities,

resulting in lower profit margins. However, this would benefit consumers, as it would

reduce the difference between buying and selling prices.

On the other hand, it could also lead to market convergence, where traders follow

the same strategies, creating a herd mentality and causing markets to move in only one

direction. This could potentially affect market stability and liquidity, especially during

times of high stress. Like any algorithm, extensive use of similar AI algorithms carries the

risk of self-reinforcing feedback loops, which can trigger major price fluctuations.

Furthermore, the use of AI in malicious activities has the potential to lead to

offensive autonomous attacks. These attacks can be carried out without human

intervention, making them even more dangerous. Not only can vulnerable systems in

trading be targeted, but financial markets as a whole, including the various participants

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in them, are also at risk. This highlights the wide scope of the potential impact of AI-

based cyberattacks.

The convergence of AI technologies not only enhances cybercriminals’ capabilities

to exploit interconnected systems but also enables the execution of autonomous attacks.

These attacks can have serious consequences for both commerce and financial markets,

requiring increased cybersecurity measures to mitigate the risks. The convergence of AI

technologies presents not only opportunities but also risks, particularly in the area of

cyberattacks. As AI systems become more interconnected and unified in their actions,

cybercriminals can exploit this unity to their advantage. They find it easier to manipulate

and influence agents that share similar behaviors than those with distinct and

differentiated behaviors. This convergence poses a significant threat to cybersecurity.

The use of proprietary models that cannot be replicated plays a crucial role in

allowing operators to maintain any form of competitive advantage. Furthermore, these

proprietary models can contribute to a deliberate lack of transparency, thus exacerbating

the challenge of understanding and explaining machine learning models. The reluctance

shown by users of machine learning techniques to disclose the effectiveness of their

models is due to the fear of compromising their competitive advantage, which in turn

raises concerns regarding the oversight of machine learning algorithms and models

(OECD, 2021).

The use of algorithms in trading can also facilitate and increase the likelihood of

collusive outcomes in digital markets (Botta & Wiedemann, 2020). Furthermore, there are

concerns that AI-based systems may worsen illegal practices aimed at manipulating

markets, such as “spoofing,” by creating difficulties for regulators to detect such activities

when machines collude. The lack of explainability of the machine learning models used

to support trading can pose difficulties in adjusting strategies during periods of poor

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trading outcomes. Trading algorithms no longer follow linear, model-based processes

(where input A leads to the execution of trading strategy B) that can be easily traced and

interpreted, making it less clear which parameters influenced the outcomes.

When considering the potential negative consequences on the market, it can be

argued that the use of AI technologies in trading and high-frequency trading (HFT) could

potentially intensify market volatility by executing large simultaneous buys or sells. This

introduces new vulnerabilities within the market. In particular, certain algo-HFT

strategies have been implicated in the emergence of extreme market volatility, decreased

liquidity, and exacerbated flash crashes, which have become more frequent in recent

years. Since HFTs play an important role in providing liquidity to the market and

improving its efficiency under normal conditions, any disruption in the operation of their

models in times of crisis may lead to a withdrawal of liquidity from the market,

potentially affecting its resilience.

In the investment arena, the widespread use of pre-existing AI models by multiple

market participants has the potential to have a major impact on market liquidity and

stability. This impact arises from the tendency of these models to encourage herding and

one-way markets. Such behavior not only magnifies the risks associated with volatility,

procyclicality, and unforeseen market swings, but also affects the scale and direction of

the market. Furthermore, herding behavior can lead to illiquid markets if there are no

“buffers” or market makers present to transact from the opposite side.

The introduction of AI into trading has the potential to create unforeseen

connections between financial markets and institutions, leading to increased correlation

and dependence of previously unrelated variables. The use of algorithms that generate

profits or returns without any correlation may actually result in the correlation of

unrelated variables if their use becomes widespread enough. Furthermore, the use of AI

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can magnify the impact of network effects, leading to unexpected changes in the size and

direction of market movements.

To address the risks associated with implementing AI in trading, it may be

necessary to put safeguards in place for AI-driven algorithmic trading. These safeguards,

built into pre-trade risk management systems, are designed to prevent and stop potential

misuse of these systems. It should be noted that AI is also being used to improve pre-

trade risk systems, encompassing mandatory testing of each algorithm version, which

applies equally to those based on AI. As a final defense for market professionals,

automated control mechanisms are in place to immediately shut down the model when

it exceeds the limits of the risk system. These mechanisms involve “pulling the plug” and

replacing any technology with human intervention. However, such measures may be

considered suboptimal from a policy perspective, as they take systems offline precisely

when they are most needed in times of stress and create operational vulnerabilities.

In addition to implementing safeguards on the exchanges where transactions take

place, it may be imperative to employ various defensive measures. These measures could

involve automatically cancelling orders whenever the AI system experiences an offline

state, as well as employing techniques that provide resilience against sophisticated forms

of manipulation facilitated by technology. There is also the possibility of modifying

circuit breakers, which are currently triggered by significant drops in trading, to also

recognize and trigger in response to a substantial volume of smaller trades executed by

AI-powered systems, thereby achieving a similar outcome.

AI: “something wheels”

Something wheels refers to a broad concept that includes fully automated solutions

designed to guide trader-driven flow. In this context, an AI-based algorithmic wheel

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represents an automated routing process that integrates artificial intelligence techniques

to assign a suitable broker algorithm to orders from a predetermined list of algorithmic

solutions (Cheng & Nowag, 2023). AI-based algorithm wheels serve as models that

determine the most advantageous strategy and broker to route the order, considering the

prevailing market conditions as well as the specific objectives and requirements of the

trading activity.

Investment firms typically use algorithmic trading wheels for two main purposes:

• First, they use these wheels to improve performance by achieving better quality of

execution.

• Secondly, they leverage algorithm wheels to streamline their workflow by

automating the handling of smaller orders and implementing standardized

naming conventions for brokers’ algorithms.

Proponents of algorithm wheels argue that they effectively mitigate traders' bias

when it comes to choosing brokers and the algorithms they implement in the market.

According to recent estimates, 20% of trade flows currently use algo wheels, a

mechanism that is gaining popularity to systematically categorize and measure the

effectiveness of algorithms used by high-performing brokers. Interestingly, those that

employ algo wheels allocate a significant 38% of their trade flow to this tool (OECD,

2021). This suggests that if algorithmic wheels were to be widely adopted, it could lead

to a significant increase in the overall volume of e-trading, which in turn could lead to

various advantages for the e-brokerage competitive landscape.

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Chapter 2

BigTech, financial services and blockchain

As tech giants continue to use their unrestricted access to vast amounts of

customer data to power AI-driven systems to deliver financial services, there is a growing

need to examine the data privacy implications of their deployment of AI. This has raised

concerns regarding the potential exploitation of the collection, storage and utilization of

personal data for commercial gain. The practices employed by BigTechs in this regard

have the potential to negatively impact customers, particularly through discriminatory

practices affecting credit availability and pricing.

BigTechs’ access to customer data gives them a significant advantage over

traditional financial services providers. This advantage is expected to be further

strengthened as they incorporate artificial intelligence into their services, enabling the

delivery of unique, personalized and more efficient offerings. However, BigTechs’

dominance in certain areas of the market can lead to over-concentration and increased

reliance on a small number of large players.

Depending on the size and scope of these companies, this could have systemic

implications and raise concerns about potential risks to financial consumers. These

consumers may not have access to the same range of product options, pricing, or advice

that would be available through traditional financial services providers. And supervisors

may face challenges in monitoring and regulating the financial activities of these big tech

companies.

Another risk related to this issue is the potential for anti-competitive behavior and

concentration of market power in the technological field of service delivery. This could

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occur if only a few dominant players emerge in the markets for AI solutions and services

using AI technologies. This trend is already being observed in certain regions of the

world. The competitive landscape is also further compromised by the advantageous

position held by large technology companies in terms of customer data (Butijn, 2023).

These companies can exploit their data advantage to establish monopolistic positions,

gaining an advantage in customer acquisition through effective price discrimination and

creating significant barriers to entry for smaller companies.

Overall, the Digital Markets Act represents a significant step towards regulating

and supervising the activities of dominant digital platforms in order to promote fair

competition and protect the interests of business users. The Digital Markets Act includes

a number of obligations that gatekeepers would be required to comply with. One of those

obligations is to provide access to data generated by their activities to business users.

Gatekeepers would also be required to offer data portability, allowing users to easily

transfer their data to other platforms. To prevent unfair competition, gatekeepers would

also be prohibited from using data obtained from business users to compete against them.

In late 2020, the European Union and the United Kingdom jointly published a set

of regulatory proposals known as the Digital Markets Act. These proposals are designed

to create a proactive framework for regulating dominant digital platforms, commonly

known as “gatekeepers,” such as large technology companies. The main goal of these

proposals is to address the risks associated with these platforms and establish fair and

open digital markets.

Another key aspect of the proposal is the introduction of measures to address the

risks associated with gatekeepers’ dual roles. This would involve implementing solutions

to address issues such as self-referral, where gatekeepers prioritize their own services

over those of third parties. The proposal also aims to ensure that services offered by

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gatekeepers are not given preferential treatment or favored over those provided by third-

party platforms.

AI and Blockchain-based financial products

In recent years, there has been a significant increase in the utilization of distributed

ledger technologies (DLT), particularly blockchain, across various sectors, with a strong

focus on the financial industry. This rise in blockchain applications can be attributed to

the numerous benefits they offer, such as increased speed, efficiency, and transparency.

These innovative technologies, driven by automation and disintermediation, have gained

traction due to their potential to revolutionize different areas, including securities

markets (such as issuance and post-trade activities), payments (such as digital currencies

and central bank stablecoins), and asset or tokenization in general. As a result, the

adoption of DLT in finance has the potential to reshape the functions and business models

of financial operators, such as custodians.

The industry is advocating for the fusion of artificial intelligence (AI) and

distributed ledger technology (DLT) in the realm of blockchain-based finance. This

integration is believed to improve the overall effectiveness of these systems by leveraging

automation to maximize the efficiency promised by blockchain-based solutions.

However, it is currently unclear whether the scope of AI implementation in blockchain-

based projects is substantial enough to substantiate claims of a true convergence between

these two technologies.

In practice, rather than seeing convergence, what we see is the implementation of

AI applications on specific blockchain systems for particular use cases, such as risk

management. We also witness the integration of DLT solutions into certain AI

mechanisms, particularly for data management purposes. Integration involves the use of

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DLT to provide input to machine learning models, taking advantage of the blockchain’s

immutability and disintermediation features.

The integration undoubtedly enables the secure sharing of sensitive information

while maintaining confidentiality and privacy. It is anticipated that the incorporation of

DLT into AI mechanisms will enable users to monetize the data they possess, which is

used by machine learning models and other AI-driven systems such as the Internet of

Things (IoT) (Moujahid, 2016). The adoption of these AI use cases is motivated by the

technology’s potential to improve automation efficiency and disintermediation in DLT-

based systems and networks.

Artificial intelligence has the potential to significantly improve the automation

capabilities of smart contracts in the realm of DLT-based finance. This contribution can

be seen in numerous use cases within DLT networks, including but not limited to

compliance and risk management. For example, AI can play a crucial role in combating

fraudulent activities by implementing automated restrictions on the network.

Furthermore, AI can also improve the functioning of oracles, which are essential for data

management and inference. However, it is important to note that these applications are

still under development and refinement.

AI has the potential to significantly improve the security and functionality of

blockchain networks, particularly in the realm of payment applications. While it cannot

completely eliminate security vulnerabilities, AI can effectively mitigate them. By

leveraging AI technology, users of blockchain networks can detect and address irregular

activities that may be indicative of theft or fraudulent behavior, although these events

typically require the compromise of public and private keys.

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Similarly, AI applications can play a critical role in streamlining onboarding

procedures, such as using biometrics for AI-based identification and strengthening anti-

money laundering and countering the financing of terrorism (AML/CFT) controls for

DLT-based financial services. Integrating AI into DLT-based systems also offers

significant benefits in terms of compliance and risk management. For example, AI-

powered tools can generate portfolio governance analysis results, which can serve as

valuable resources for compliance purposes or internal risk assessments of transaction

parties. However, it is important to recognize that removing financial intermediaries

from financial transactions can undermine the effectiveness of existing regulatory

approaches that focus primarily on regulated entities.

By incorporating AI-powered solutions into distributed ledger technology (DLT)

systems at the protocol level, regulators can effectively achieve their regulatory

objectives. This can be achieved through several means, such as facilitating seamless, real-

time data exchange between entities and regulated authorities, as well as incorporating

regulatory requirements directly into program code to ensure automatic compliance. The

concept of regulators becoming nodes in decentralized networks has been a topic of

discussion within the market, as it presents a potential solution to the challenges of

overseeing platforms that operate without a central authority.

In relation to data in DLT-based systems, AI has the potential to enhance these

processes. By shifting the responsibility for data curation from third-party nodes to

independent, automated AI-powered systems, the quality of data fed into the chain can

be significantly improved. This, in turn, leads to more robust recording and sharing of

information, as AI systems are less prone to manipulation. One area where AI can make

a particular difference is in the operation of third-party off-chain nodes, commonly

known as “Oracles,” which play a crucial role in feeding external data into the network.

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The use of oracles in distributed ledger technology (DLT) networks exposes a

potential risk of erroneous or inappropriate data being entered into the network, arising

from the possibility of malicious or poorly performing third-party off-chain nodes. To

address this issue, integrating artificial intelligence (AI) on-chain could improve

disintermediation by making third-party information providers, such as oracles,

unnecessary.

By incorporating AI, the system can verify the accuracy and integrity of the data

provided by the oracles, thus preventing cyberattacks and manipulation of third-party

data supply within the network. Implementing AI applications could potentially improve

the trust of participants in the network, as they can verify the information provided by

the oracles and identify any compromise within the system. However, it is important to

note that AI does not inherently solve the problem of poor quality or inadequate input

data, as this challenge is also present in AI-based mechanisms and applications.

AI increases the capabilities of smart contracts

The integration of AI techniques into blockchain-based systems has the potential

to bring about significant changes, particularly in the realm of smart contracts. This

integration may have practical implications on the governance and risk management of

these contracts and may introduce several hypothetical, yet-to-be-tested effects on the

functions and processes of distributed ledger technology (DLT)-based networks. Using

AI in this context may pave the way for self-regulating DLT chains that operate

autonomously.

Smart contracts have been around for a considerable time, predating the

emergence of AI applications, and operate with simple software code. Currently, most

widely used smart contracts do not incorporate AI methods. Consequently, numerous

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proposed advantages of incorporating AI into DLT systems remain primarily theoretical,

and it is advisable to approach claims made by industry regarding the integration of AI

and DLT capabilities into commercialized products with a sense of careful consideration.

That said, the use of AI in various scenarios is highly advantageous when it comes

to improving the capabilities of smart contracts, particularly in the areas of risk

management and identifying flaws within the code. AI methodologies such as natural

language processing (NLP) can effectively assess the execution patterns of smart

contracts, thereby enabling the detection of fraudulent activities and improving the

overall security of the system.

AI also has the ability to perform code testing in a way that surpasses the

capabilities of human code reviewers in terms of speed and level of detail, as well as

scenario analysis. Since code forms the fundamental basis of smart contract automation,

the flawless nature of the coding process is crucial to ensuring the resilience and

reliability of these contracts.

The potential to enhance the automation capabilities of smart contracts by

integrating AI is a promising concept. By incorporating AI into smart contracts, the level

of autonomy can be increased, allowing the underlying code to dynamically adapt to

changing market and environmental conditions. A specific area of AI, known as natural

language processing (NLP), has the potential to expand the analytical capabilities of

smart contracts, particularly in relation to traditional contracts, legislation, and court

rulings. By leveraging NLP, smart contracts can gain deeper insights into the intentions

of the parties involved. While it is worth mentioning that these applications of AI for

smart contracts remain purely theoretical and have yet to be tested in real-world

scenarios.

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There are still challenges that need to be addressed when it comes to operational

risks, as well as compatibility and interoperability between traditional infrastructure and

one based on DLT and AI technologies. The use of AI techniques, such as deep learning,

requires a significant amount of computational resources, which can hinder their

effectiveness on Blockchain. Some experts argue that at the current stage of infrastructure

development, it would be better to store data off-chain to ensure that real-time

recommendation engines function properly and to minimize latency and costs. The

operational risks associated with DLT are still unresolved and will need to be addressed

as both the technology and the applications it enables continue to mature.

Smart contracts in DLT-based systems:

• These are decentralized applications that are built and deployed on the blockchain.

These applications are made up of self-executing contracts that are written as code

on the blockchain ledger. They are designed to automatically execute when certain

predetermined events occur, which are also written into the code. This technology

has been recognized and discussed by the Organization for Economic Cooperation

and Development in 2019.

• They are computer programs that run on the Ethereum blockchain. These

programs are designed to determine the functioning and timing of certain actions.

Like traditional contracts, they establish a set of rules that are then automatically

enforced through the use of code, only when predefined conditions are met.

• They operate autonomously on the network and run on a predetermined schedule,

eliminating the need for user control. Users can participate in smart contracts by

initiating transactions that trigger specific functions described in the contract.

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• They play a crucial role in enabling the disintermediation that distributed ledger

technology (DLT) networks can leverage. They offer an important source of

efficiency that these networks promise to deliver. By enabling the automation of

various actions such as payments and asset transfers based on predefined

conditions recorded in code, smart contracts eliminate the need for human

intervention. However, despite their potential benefits, the legal status of smart

contracts is still uncertain in many areas. They are not yet widely recognized as

legal contracts. This lack of clarity raises concerns about enforceability and

financial protection when it comes to smart contracts. Furthermore, auditing the

code of these contracts requires additional resources from market participants

who want to ensure the legitimacy and trustworthiness of the underlying

processes.

Self-learning smart contracts and DLT governance

According to the researchers, AI-powered smart contracts have the potential to

create self-regulating chains. In the future, AI could be used to forecast and automate

processes within “self-learning” smart contracts, similar to the application of

reinforcement learning AI techniques. AI can extract and analyze real-time information

from systems and incorporate that data into smart contracts. As a result, smart contract

code could automatically adapt, eliminating the need for human intervention in chain

governance. This would lead to the establishment of fully autonomous and self-

regulating decentralized chains.

Decentralized autonomous organizations (DAOs) are entities that operate

autonomously on a blockchain, and while they have already been established,

incorporating AI-based techniques could enhance their functionality. For example, AI

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could provide real-time data to the code, allowing it to calculate the optimal course of

action. Self-learning smart contracts that integrate AI would play a crucial role in

expanding the capabilities of blockchain logic. These contracts would learn from the

blockchain’s past experiences, adapt or introduce new rules, and govern the overall

functioning of the blockchain.

Currently, most DeFi projects are run by DAOs that possess certain centralized

elements, such as on-chain voting by token holders and off-chain consensus. These

elements involving human intervention could be subject to regulation. However, by

integrating AI into DAOs, it is possible to enhance decentralization and decrease the

relevance of traditional regulatory approaches.

Utilizing AI in building fully autonomous chains presents significant hurdles and

uncertainties for both users and the ecosystem at large. In such environments, the

execution of decisions and the operation of systems would be entrusted to AI smart

contracts instead of human involvement, giving rise to crucial ethical concerns (Butijn,

2023). Moreover, implementing automated mechanisms to instantly deactivate the model

is particularly challenging in such decentralized networks, which is a major issue faced

by the DeFi space as well.

The inclusion of artificial intelligence (AI) in blockchains has the potential to

benefit decentralized finance (DeFi) applications by streamlining processes and

improving efficiency in the delivery of various financial services. For example, the

integration of AI models can enable personalized recommendations for users on different

products and services, facilitate online data-driven credit scoring, and offer data-driven

investment and trading advisory services. Likewise, the application of reinforcement

learning in blockchain-based processes opens up more possibilities for AI in DeFi. It is

worth noting that the incorporation of AI into DeFi can expand the capabilities of

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distributed ledger technology (DLT) use cases by introducing new functionalities. While

it is important to recognize that the introduction of AI may not completely transform the

underlying business models in these applications.

AI for ESG investing

ESG ratings

Data can differ significantly between various ESG rating providers

due to the use of different frameworks, measures, key indicators and metrics, as well as

subjective judgement and weighting of subcategories. This disparity in ratings is further

exacerbated by the lack of necessary tools, such as consistent data, comparable metrics

and transparent methodologies, which are crucial to inform decision-making in the

market. The importance of data becomes even more crucial when analyzing non-financial

aspects of company performance related to sustainability issues. However, concerns

remain regarding the quality of ESG data, including gaps in data availability, potential

inaccuracies and the lack of comparability across different providers.

Artificial intelligence and big data have the potential to revolutionize ESG

investing by providing a means to assess company data, non-trading data and the

consistency and comparability of ratings. The use of AI can significantly improve

decision-making by reducing subjective and cognitive biases that often arise from

traditional analysis methods, as well as minimizing noise in ESG data and leveraging

unstructured data. Natural language processing (NLP) can specifically analyze large

amounts of unstructured data, such as geolocation and social media information, to

perform sentiment analysis and identify patterns and relationships. These analyses can

The ESG score is determined by collecting and analyzing data related to different aspects and evaluating them

based on a predefined scale. The ESG score evaluates a company's achievements and actions in different areas,

including, but not limited to, emissions control, protection of human rights, and promotion of sustainable

purchasing practices.

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then be used to assign quantitative values to qualitative sustainability parameters, using

advanced AI techniques.

Recent years have seen the rise of alternative ESG rating providers, with these

providers offering AI-powered ratings to facilitate a more impartial external assessment

of companies’ sustainability performance. By harnessing the power of AI, these rating

providers aim to address the problem of greenwashing, which occurs when companies

adopt superficial sustainability measures while continuing with their conventional

business strategies. By using AI, these providers can uncover crucial insights into

companies’ sustainability practices and actions that would otherwise not be easily

accessible. This innovative approach has immense potential to improve transparency and

accountability in the business world.

There is empirical data available to support the use of alternative AI-based ESG

ratings. This evidence suggests that these ratings have several key benefits compared to

traditional approaches. These advantages include a higher level of standardization, a

more democratic aggregation process, and the use of rigorous real-time analytics.

However, these methods are unlikely to completely replace traditional models in the

future. Instead, they have the potential to work alongside traditional ESG rating

approaches, providing additional insights to investors about undisclosed information

about the entities being rated.

It is therefore important to recognize that reputable companies themselves can use

AI to potentially obscure their sustainability performance. By leveraging AI techniques,

these entities can gain a deeper understanding of their operations and accurately identify

areas that need to be strategically highlighted in terms of disclosure to improve their

environmental, social and governance (ESG) ratings. This allows them to manipulate

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their ESG ratings by emphasizing these specific areas, creating a distorted picture of their

overall sustainability performance.

Emerging risks from AI/ML/Big Data use: risk mitigation tools

The expansion and diversification of AI/ML technology in financial markets has

generated numerous challenges and risks that require careful attention from market

participants, industry professionals, and policymakers. These challenges can be observed

at various levels, including the data level, the model and business level, as well as the

societal and systemic level.

In the rapidly expanding field of AI in finance, there are several challenges that

deserve careful attention and consideration. These challenges include data management

and concentration, the potential for bias and discrimination, the need for explainable AI

models, ensuring the robustness and resilience of AI systems, establishing effective

governance and accountability frameworks, addressing regulatory concerns, and

managing occupational risks and skills. To mitigate these risks, it is important to explore

potential strategies and solutions.

Data and its management

Data plays a central role in all ANN applications driven by AI, machine learning

models and big data presents numerous possibilities to improve efficiency, reduce costs

and increase customer satisfaction by offering superior services and products (Moujahid,

2016).

The risks that arise when using big data in AI-powered ANN in the financial sector

stem from several factors, including the quality of the data used, concerns about data

privacy and confidentiality, cybersecurity threats, and fairness and equity considerations.

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A key risk is the potential for unintentional bias and discrimination against certain

groups of people, which can occur when data is misused or when inappropriate data is

used in models, such as in credit underwriting. In addition to consumer protection

concerns in the financial space, the use of big data and machine learning models can also

raise competition concerns, particularly if there is a high concentration of market

providers.

The representativeness and relevance of the data

One of the key aspects of big data, commonly referred to as one of the four “Vs,”

is veracity, referring to the level of uncertainty surrounding the reliability and accuracy

of big data. This uncertainty can arise from a number of factors, such as questionable

sources, inadequate data quality, or the inappropriateness of the data being used. In the

big data realm, the veracity of observations can be influenced by specific behaviors

observed on social media platforms, the presence of noisy or biased data collection

systems, such as sensors or the Internet of Things (IoT). As a result, the veracity of big

data may not be sufficient to prevent or mitigate disparate dynamic impacts, further

complicating its reliability and usability.

The concept of data representativeness and relevance is more significant when it

comes to AI applications (ANN) than data veracity. Data representativeness focuses on

whether the data used provides a comprehensive and balanced representation of the

population being studied, including all relevant subgroups. This is particularly important

in financial markets to ensure that certain groups of traders are not over- or under-

represented, leading to more accurate model training.

In the context of credit scoring, data representativeness can help promote financial

inclusion among minority groups. On the other hand, data relevance refers to the degree

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to which the data used accurately describes the phenomenon under study without

including misleading information. For example, when evaluating credit scores, it is

essential to carefully assess the relevance of information about the behavior or reputation

of individuals or legal entities before incorporating it into the model. However,

evaluating the data set on a case-by-case basis to improve accuracy and appropriateness

can be cumbersome due to the huge volume of data involved, which could undermine

the efficiency gained from implementing AI.

Privacy and confidentiality of data

The use of data in AI systems raises several concerns regarding data protection

and privacy due to its volume, prevalence, and continuous flow. Aside from typical

concerns about the collection and utilization of personal data, the field of AI introduces

additional complexities. For example, AI’s ability to draw inferences from extensive data

sets can create compatibility issues. Traditional privacy practices such as “notice and

consent” may not be practical for ensuring privacy protection in machine learning

models.

There are also challenges related to data connectivity and the transfer of data

across borders. This emphasizes the importance of data connectivity in the financial

industry and the critical need to have the ability to aggregate, store, process and transmit

data internationally while implementing appropriate safeguards and governance

standards.

The process of merging multiple data sets can present numerous advantages for

those who are new to working with big data (Harrington, 2018). By combining data from

diverse sources, people can get a broader and more complete picture of the information

they are analyzing. This is particularly beneficial when dealing with databases that have

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been collected under different conditions, such as different populations, regulations, or

sampling methods. By bringing together these diverse data sources, new analytical

opportunities arise that would not have been possible by examining each data set

individually. However, it is important to note that merging heterogeneous data sets also

introduces certain challenges and complexities. For example, combining data from

different settings can lead to confounding factors, biases in sample selection, and biases

that arise when comparing different populations.

The presence of cybersecurity risks, hacking risks, and other operational risks in

the field of digital financial products and services has a direct impact on data privacy and

confidentiality (Brynjolfsson et al., 2019). While the use of AI technology does not

introduce new avenues for cyber breaches, it has the potential to amplify existing

vulnerabilities. This amplification can occur through various means, such as the

connection between falsified data and cyberattacks, leading to the emergence of new

attacks that can alter the functionality of the algorithm by introducing manipulated data

into its models. Furthermore, AI can also facilitate the modification of already existing

attacks.

The sharing and use of consumers’ financial and non-financial information is

becoming more frequent, often without their full understanding or consent. While

obtaining informed consent is the legal basis for using data, consumers may not always

be aware of how their information is handled or where it is used, leading to potential

gaps in their understanding and consent. The increased use of advanced tracking

methods to monitor online activities and data sharing by third-party vendors further

increases these risks. Thus, also data sets that are collected without direct customer input,

such as geolocation or credit card transaction data, are particularly susceptible to

potential breaches of privacy policies and data protection laws.

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The industry is suggesting new methods to protect consumer privacy when

calculating confidentiality. One approach is to create and use custom synthetic datasets

for machine learning purposes. Another approach is to use privacy-enhancing

technologies (PETs), which aim to maintain the general characteristics of the original data

while keeping individual data samples confidential. PETs encompass techniques such as

differential privacy, federated analysis, homomorphic encryption, and secure multi-party

computation. Differential privacy, in particular, offers stronger privacy guarantees and

enables more accurate calculations compared to synthetic datasets. The advantage of

these techniques is that models trained on synthetic data perform as well as those trained

on real data. Traditional data anonymization methods, on the other hand, do not offer

strong privacy guarantees, especially considering the inferences made by AI models.

The use of big data in AI-based models has the potential to expand the scope of

what is considered sensitive information. These models have the ability to accurately

identify individual users by efficiently analyzing a variety of factors, such as facial

recognition technology and inferred data, such as customer profiles. By combining this

information with other data sources, AI models can make inferences about user

characteristics, such as gender, or even re-identify individuals from anonymized

databases by cross-referencing them with publicly available information. This process

leads to the attribution of sensitive information to specific individuals. Furthermore, the

increased dimensionality of ML datasets, which allows for an unlimited number of

variables to be considered compared to traditional statistical techniques, increases the

likelihood of including sensitive information in the analysis.

Regulators have therefore rekindled the focus on privacy and data protection due

to the increasing digitalization of the economy. One such example is the European

Union’s General Data Protection Regulation (GDPR), which seeks to enhance consumer

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protection and rebalance the power dynamics between businesses and individuals. The

overall aim is to empower consumers and foster transparency and trust in how businesses

handle consumer data. Safeguarding consumer data and privacy is a core principle

outlined in the G20-OECD High Level Principles on Financial Consumer Protection.

Furthermore, the Monetary Authority of Singapore is committed to upholding fairness,

ethics, accountability and transparency in the use of artificial intelligence in the financial

sector, with a particular emphasis on protecting individuals’ personal data.

The financial sector faces challenges in improving data governance for businesses

due to the perception of fragmentation in regulatory and supervisory responsibility

regarding data. There is uncertainty about which institutions should implement the most

effective data governance practices, including areas such as data quality, definitions,

standardization, architecture and de-duplication. This issue becomes even more complex

when cross-border activities are considered.

The field of data use in economics is undergoing a transformation due to the

widespread adoption of machine learning models in the financial industry (Levenstein &

Suslow, 2006). As a result, a few companies specializing in alternative data sets have

emerged to meet the growing demand for data to serve as the basis for AI techniques.

However, these data set brokers operate with minimal oversight and transparency,

raising concerns about the legality of financial service providers’ acquisition and use of

their data. Furthermore, the rising compliance costs associated with regulations designed

to protect consumers may have a profound impact on the economics of big data use in

the financial market. This, in turn, will influence how financial market providers

approach the use of AI and big data.

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Chapter 3

Data and competition in AI-based financial services

Advances in AI have the potential to create competitive advantages that could

negatively impact efficient and competitive markets, as consumers may have limited

ability to make informed decisions if there is a high concentration of market providers.

The use of AI may give certain financial services providers an advantage over smaller

competitors who may not have the resources to adopt these technologies. In addition,

uneven access to data and the dominance of a few large BigTech companies in obtaining

big data could make it difficult for smaller players to compete in the market for AI-based

products and services.

The risks of concentration and dependence on a few dominant players are

heightened by the potential for network effects, which could result in the emergence of

new players that have a significant impact on the entire system. BigTechs, in particular,

exemplify this risk, and the fact that they operate outside regulatory boundaries further

complicates the challenges associated with this issue. This is primarily due to the way in

which big tech companies access and use data, which is further amplified by the use of

artificial intelligence techniques to generate profits from that data. In addition, we are

witnessing a growing influence of a small number of alternative data providers in the

database industry, which could lead to a concentration of power in that market.

When it comes to entering the AI market, smaller companies may encounter data-

related hurdles, as they may need to invest in expensive tools such as advanced data

mining software and machine learning technology, as well as physical infrastructure such

as data centers. Investments are more cost-effective for larger companies due to

economies of scale. Additionally, algorithms need to access a wide range of data from

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diverse sources to identify new relationships and patterns. Since smaller companies

without the necessary resources or a presence in multiple markets may struggle to

develop algorithms that can effectively compete with established players, they may face

barriers to entry that hinder their ability to succeed in the AI market.

The presence of healthy competition in the market for AI-based financial products

and services is crucial for providers to fully leverage the advantages of this technology,

particularly in trading and investment (Descamps et al., 2021). The use of third-party or

outsourced vendor models can help determine the advantages of these tools for the

companies that implement them, but it also has the potential to create one-sided markets

and encourage herding behavior among financial consumers. Thus, financial

professionals may begin to adopt similar trading and investment strategies, resulting in

convergence within the industry.

Tacit collusion: risks

The implementation of AI-based models on a large scale could raise concerns

about competition as it allows for the possibility of tacit collusion without any formal

agreement or human interaction (Caforio, 2023). Tacit collusion refers to a situation where

competitors independently decide on strategies to maximize their own profits, leading to

a non-competitive outcome. In simpler terms, the use of algorithms makes it easier for

market participants to maintain profits above the competitive level without explicitly

colluding, replacing explicit collusion with tacit coordination.

Although tacit collusion typically occurs in markets that are transparent and have

a limited number of participants, there is evidence to suggest that collusion becomes

more manageable and observable in digital markets involving algorithms. These digital

markets are characterized by a high level of transparency and frequent interaction.

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The ability of self-learning and deep learning AI models to adapt and learn

dynamically has the potential to increase the risk that these models recognize and adjust

to the behavior and actions of other market participants or AI models. This could lead to

collusive outcomes being reached without any human intervention, and even without the

AI model itself being aware of it. While such collusion does not necessarily violate

competition laws, it raises concerns about how to address and regulate the model and its

users through enforcement measures.

Bias and discrimination

AI methods have the ability to mitigate discrimination by humans in various

interactions or amplify prejudice, unfair treatment, and discrimination in the financial

services arena. By entrusting the algorithm with the responsibility of decision-making,

people using AI-based models can avoid the inherent biases associated with human

judgment. However, it is essential to recognize that the adoption of AI applications also

presents the possibility of introducing bias or discrimination due to the potential

reinforcement of existing biases found in the data. This can occur through training

models using biased data or identifying misleading correlations.

Using faulty or inappropriate data in neural networks can lead to incorrect or

biased decision-making (Tan, 1997). When poor-quality data is used, biased or

discriminatory decisions can be made in two ways. Machine learning models that are

trained on inadequate data can produce inaccurate results, even when good-quality data

is input. Similarly, machine learning models trained on high-quality data can still

produce questionable results if fed inadequate data, despite being well-trained

algorithms. Consequently, well-intentioned ML models can unintentionally produce

biased conclusions that discriminate against certain protected groups. Using incorrect,

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inaccurate (such as mislabeled or incomplete), or fraudulent data in machine learning

models poses the risk of “garbage in, garbage out,” where the quality of the model output

is highly dependent on the quality of the data.

Biases may also exist in the data used as variables, and because the model is trained

on data from external sources that may have already incorporated certain biases, it

continues to retain these historical biases. Likewise, biased or discriminatory decisions

made by machine learning models are not necessarily intentional and can occur even with

high-quality, well-labeled data. This can happen through inferences and proxies, or

because it is difficult to identify correlations between sensitive and non-sensitive

variables within large databases. Since big data encompasses vast amounts of information

that reflect society, AI-based ANNs have the potential to perpetuate existing biases

present in society and reflected in these databases.

The involvement of humans in AI-based decision-making processes is crucial to

detect and rectify any bias that may be present in the data or in the model design.

Furthermore, humans play a vital role in interpreting and explaining model outputs,

although the feasibility of achieving this to its fullest extent is still uncertain. The human

factor is essential both at the data entry and system query stages, and it is important to

approach model outputs with a certain level of skepticism to minimize the potential risks

of biased results or decision-making.

ML model design and auditing play a crucial role in ensuring model robustness

and minimizing potential bias. If AI/ML models are not intelligently designed and

controlled, they can unintentionally amplify existing biases and make it even harder to

detect discrimination. Conducting model and algorithm audits that compare model

outputs to benchmark data sets can help prevent unfair treatment and discrimination.

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It is essential that scoring systems can be tested by users and supervisors to ensure

fairness and accuracy. Tests can also be run to see if protected classes can be inferred from

other attributes in the data, and various techniques can be used to identify and rectify

bias in ML models. It is also important to govern AI/ML models and assign responsibility

to humans involved in the project to protect potential borrowers from unfair bias. When

assessing bias, it is critical to avoid comparing machine learning-based decision making

to an ideal unbiased state and instead use realistic benchmarks comparing these methods

to traditional statistical models and human-based decision making, as both approaches

have their limitations and potential biases.

Explainability

One of the main challenges facing ML models is the difficulty in breaking down

the model’s output and understanding the factors that contribute to its decision-making

process. This concept, known as “explainability,” refers to the ability to justify or

rationalize the decisions and outcomes produced by the model. AI-based models are

inherently complex due to the nature of the technology used, and the intentional

concealment of the inner workings of these models by market players further increases

the lack of explainability. Simply having access to the underlying code is not enough to

fully understand the mechanics of the model, especially considering the widespread lack

of technical knowledge among end consumers. This problem is further exacerbated by

the mismatch between the complexity of AI models and the cognitive capabilities of

humans in terms of reasoning and interpretation.

The lack of trust that users and supervisors have in AI applications is due to the

inability to understand and explain how machine learning models work. In the field of

finance, AI-driven approaches have become increasingly complex and opaque, making it

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difficult for people to understand how these models make decisions. Even if the

underlying mathematical principles of these models can be explained, they still lack a

clear and explicit explanation of their knowledge.

This lack of transparency therefore undermines trust between financial consumers

and regulators, especially in critical financial services. To address this issue, improving

the explainability of AI applications is critical as it can help maintain trust in the industry.

From an internal control and governance perspective, it is important to ensure that AI

models have a minimum level of explainability. This allows a modelling committee to

thoroughly analyze the model and feel confident in its implementation.

Thus, the absence of explainability may not be consistent with existing regulations

that require understanding and disclosure of the underlying logic. For example,

regulations may require comprehensive understanding and explanation of algorithms

throughout their lifespan. Other policies may give individuals the right to receive an

explanation of decisions made by algorithms, along with information about the logic

involved, such as the GDPR in the EU19, which applies to credit decisions and insurance

pricing.

Another example is the potential use of ML to calculate regulatory requirements,

such as risk-weighted assets (RWA) for credit risk. Current standards require that these

models be explainable or at least subject to human oversight and judgment, as described

in the Basel Framework for calculating RWAs for credit risk.

The use of ML-based models in financial markets could pose a significant risk if

regulators do not closely monitor their lack of explainability. This lack of transparency

makes it difficult for both financial firms and supervisors to anticipate how these models

will impact markets. This is particularly worrying because AI technology has the

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potential to introduce or amplify systemic risks, such as the increased likelihood of

herding behavior and strategy convergence among users of generic models provided by

third-party vendors.

Without a deep understanding of how these models work, users have limited

ability to predict their impact on market conditions or identify whether they are

contributing to disruptions. Furthermore, users are unable to adapt their strategies in the

face of poor performance or market stress, which can lead to increased market volatility

and periods of illiquidity. This can further exacerbate events such as flash crashes. Lack

of a clear understanding of model mechanics also creates risks of market manipulation,

such as spoofing and tacit collusion between market participants.

Financial professionals in the field of financial markets who use AI-based ANNs

are facing increased scrutiny regarding their ability to explain how their models work.

This increased attention has led many market participants to focus on improving the

explainability of these models. In doing so, they hope to better understand how the

models behave both under normal market conditions and times of stress, as well as

effectively manage the associated risks.

While achieving explainability by design, i.e., building explainability into the AI

mechanism itself, is a challenging task. This is primarily due to several reasons: first, the

general public may have difficulty understanding the underlying logic of the model;

second, some models, such as certain neural networks, are inherently complex and

cannot be fully understood; and third, fully disclosing the mechanism would involve

disclosing the intellectual property behind the model (Motta, 2023).

The issue of explainability in relation to AI has sparked a thought-provoking

debate about how the level of explainability required for AI differs from that needed for

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other complex mathematical models in the financial sector. One concern is that AI

applications may be seen as more demanding, leading to a higher burden of

explainability compared to other technologies. This potential disparity could have a

detrimental effect on innovation within the field. Rather than focusing solely on the

mathematical potential of AI models, it is crucial that committees prioritize the analysis

of the inherent risks that these models may expose businesses to, determining whether

these risks can be effectively managed.

Financial service providers need to strike the right balance between model

explainability and accuracy/performance in order to navigate the trade-off between the

two. It is crucial that these providers have a certain level of understanding of how the

model operates and the underlying logic it follows to avoid being perceived as “black

boxes”. By having this knowledge, financial service providers can comply with

regulatory obligations and build trust with consumers. In certain areas such as Germany,

models that lack some degree of explainability are not accepted.

It is important to note that there is no single principle or approach that can fully

explain ML models, and the level of explainability will vary depending on the specific

context. When assessing the interpretability of a model, it is necessary to consider the

question being asked and the predictions made by the model. Furthermore, it is critical

to understand that ensuring the explainability of the model does not automatically

guarantee its reliability. To effectively align explainability with the public, a shift in focus

towards “risk explainability” is necessary. This means placing more emphasis on

understanding the potential risks associated with using the model, rather than focusing

solely on the methodology behind the model. The UK Information Commissioner’s Office

has recently provided guidance on using five contextual factors (scope, impact, data used,

urgency and audience) to assess the type of explanation required.

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The auditability of algorithms

The use of “black box” models in financial services, such as lending, presents

challenges when it comes to regulatory transparency and auditing. These models are

complex and difficult to decompose, making it impossible to understand the underlying

drivers of their output. This lack of explainability hampers the ability to perform audits

and limits the supervisor’s understanding of the model’s decision-making process. In

some areas, laws and regulations require auditability and transparency, which can be

difficult for ANNs to achieve. The ability to follow an audit trail depends on the

interpretability of the model, which is often limited in AI models. As the decisions made

by these models are already non-linear and their interpretability is limited, it is crucial to

find ways to improve the explainability of AI outputs while maintaining accountability

and strong governance in AI-based systems.

There are ongoing research efforts in both academia and industry aimed at

improving the understandability of artificial intelligence (AI) applications and making

machine learning (ML) models more accessible for examination before and after

deployment.

The disclosure

The OECD AI-powered ANN Principles emphasize the importance of

transparency and responsible disclosure when it comes to AI systems. This means that it

is crucial for people to have a clear understanding of AI-based results and to have the

ability to question or challenge them. To address the issue of opacity of algorithm-based

systems, it is proposed to implement transparency requirements. This would involve

providing clear information about the capabilities and limitations of the AI system. The

purpose of separate disclosure is to inform consumers about the use of AI in the delivery

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of a product and their interaction with an AI system rather than a human, as in the case

of robo-advisors. By having this information, customers can make informed decisions

and choose between different competing products.

So far, there is no widely accepted standard on the amount of information that

should be disclosed to investors and financial consumers, as well as on the

proportionality of such information. Market regulators suggest that the level of

transparency should vary depending on the type of investor (retail or institutional) and

the area of application (front or back office). These regulators argue that suitability

requirements, such as those applied to the sale of investment products, could help firms

assess whether potential customers have a comprehensive understanding of how the use

of AI affects the provision of the product or service.

Requirements for financial firms to document operational details and design

features of financial models existed even before the emergence of AI. Some regulators are

now using documentation of algorithm logic as a means to ensure that model outputs

can be explained, traced and repeated.

The European Union, for example, is contemplating implementing requirements

to disclose documentation on methodologies, processes, programming, and training

techniques used in the development, testing, and validation of AI systems, including

documentation on the algorithm itself (OECD, 2021). The US Association for Computing

Machinery (USACM) Public Policy Council has proposed a set of principles prioritizing

transparency and auditability in the use of algorithms, suggesting that models, data,

algorithms, and decisions should be recorded to allow for auditing in case of suspected

harm. The Federal Reserve’s guidance on model risk management also emphasizes the

need for detailed documentation of model development and validation, allowing people

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unfamiliar with the model to understand its operation, limitations, and assumptions

(OECD, 2021).

Financial service providers are facing increasing challenges when it comes to

documenting the process of AI-based models used for monitoring purposes. This

difficulty arises from the complex nature of these models, which makes it difficult to

explain how they work and subsequently document them. This challenge is not limited

to the size of the service provider, as even smaller providers face the same obstacles.

In response, some areas have proposed a two-pronged approach to overseeing AI

models:

• The first aspect is analytical and combines source code and data analysis to

document AI algorithms, predictive models and data sets, preferably following

standardized methods.

• The second aspect is empirical and uses techniques that provide explanations for

individual decisions or the general behavior of the algorithm.

This is achieved through the use of challenger models, which are used to compare

against the model being tested, as well as benchmarking data sets selected by auditors.

Furthermore, aside from the difficulties associated with explaining AI-based

models, there is the added complexity of setting numerous parameters that affect model

performance and outcomes. The parameterization process can be considered “arbitrary”

and subjective, as it is often based on intuition rather than thorough validation and is

heavily influenced by the individual designing the model. While revealing the chosen

parameters might alleviate some of the problems, explaining how these parameters

interact with the model still poses a significant challenge.

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Robustness and resilience of AI models

It is crucial for AI systems to operate effectively, safely, and reliably at every stage

of their existence, and it is imperative to continuously assess and mitigate any potential

risks they may pose. To improve the robustness of AI systems, it is essential to diligently

train models and thoroughly evaluate their performance in line with intended objectives.

Training AI models

To account for more complex relationships and non-linearity in the data, it may be

necessary to train models using larger datasets. This is because higher-order interactions

may be harder to reach and require more data to discover. Therefore, it is crucial to have

sufficiently large da